Patient variability presents a significant challenge to pharmacology but is key to determining the connection between genotype and disease phenotype. With the rise in individual risk factors, there is a possibility of adverse cardiovascular events, as well as associated morbidity and mortality. This project focuses on computational modeling at an atomistic scale to advance digital twin technology for patient-specific treatment of cardiovascular disease. We investigate the voltage-gated L-type calcium channel, Ca V 1.2, responsible for the plateau phase of the cardiac action potential. The L-type calcium channels regulate influx of calcium, which influences the contraction of blood vessels, making these channels the targets for anti-hypertensive calcium channel blocker (CCB) drugs. The efficacy and cardiac safety of Ca V 1.2 may be altered by missense mutations in the CCB binding pocket (BP), such as G402S, T1056Y, and E1135K. Structural models of wild-type (WT) and mutant Ca V 1.2 generated using AlphaFold revealed BP variations for each of the three representative CCB subclasses. Structural alignment showed that root mean square deviations (RMSDs) of our models remain consistent with experimental Ca V 1.1 and Ca V 1.2 structures, while mutations introduce small RMSD differences within the CCB BPs ∼0.4–1.8 Å. Both drug docking and flooding MD simulations will be conducted to confirm whether the RMSD deviations affect CCB binding. Additionally, we will explore virtual screening using RosettaVS toward the discovery of potential novel Ca V 1.2 small molecule inhibitors, which can mitigate Ca V 1.2 mutation effects. This work will enable the development of new, efficient, and patient-specific therapeutic strategies and medications for cardiovascular disorders.
Siritanapivat et al. (Sun,) studied this question.